Analysts in various professions may, at times, be called upon to search relatively large collections of imagery to identify, if present, various types of relevant information (referred to herein as “a target entity” or “target entities”) in the collection of imagery. For example, medical analysts sometimes diagnose a physical impairment by searching complex imagery collections to identify one or more target entities therein that may be the cause of the physical impairment. Moreover, intelligence analysts may be called upon to search relatively complex imagery collections to identify target entities therein that may relate to various types of intelligence gathering activities.
Advancements in both image collection and storage technology presently allow for the relatively low-cost storage of large volumes of high-quality imagery. However, the cost of searching through large sets of imagery for target entities can often be substantial. Indeed, in many professions, such as intelligence gathering, effective searching may rely on the expertise of highly skilled analysts, who typically search through relatively large sequences of images in a relatively slow manner. Presently, the number of skilled analysts available to search the amount of imagery that is stored, or can potentially be stored, is in many instances insufficient.
In response to the foregoing, there has relatively recently been a focus on developing various systems and methods for triaging imagery. One of the methods that has shown promise combines electroencephalography (EEG) technology and rapid serial visualization presentation (RSVP). Various implementations of this combination have been researched and developed. For example, researchers have experimented with a system in which users are presented, using the RSVP paradigm, a sequence of images, some of which may include particular types of target entities. During the RSVP presentation, EEG data are collected from the users. A classifier then uses the collected EEG data to assign probabilities to each image. The probabilities are representative of the likelihood an image includes a target.
Although useful in sorting a sequence of images, the above described system and method, as well as other systems and methods that employ these same technologies, do suffer certain drawbacks. For example, the likelihood that a user correctly identifies a target in an image can decrease significantly for targets located at or near the periphery of a user's visual field. It is believed that the brief presentation rates provide little opportunity for eye saccades to search the entire span of an image.
Hence, there is a need for a system and method of generating images for presentation to a user in which potential targets are located at least more closely to the center of the displayed images. The present invention addresses at least this need.